6,938 research outputs found
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Variable neighbourhood search for the minimum labelling Steiner tree problem
We present a study on heuristic solution approaches to the minimum labelling Steiner tree problem, an NP-hard graph problem related to the minimum labelling spanning tree problem. Given an undirected labelled connected graph, the aim is to find a spanning tree covering a given subset of nodes of the graph, whose edges have the smallest number of distinct labels. Such a model may be used to represent many real world problems in telecommunications and multimodal transportation networks. Several metaheuristics are proposed and evaluated. The approaches are compared to the widely adopted Pilot Method and it is shown that the Variable Neighbourhood Search that we propose is the most effective metaheuristic for the problem, obtaining high quality solutions in short computational running time
Variable neighbourhood search for the minimum labelling Steiner tree problem
We present a study on heuristic solution approaches to the minimum labelling Steiner
tree problem, an NP-hard graph problem related to the minimum labelling spanning tree
problem. Given an undirected labelled connected graph, the aim is to find a spanning
tree covering a given subset of nodes of the graph, whose edges have the smallest number
of distinct labels. Such a model may be used to represent many real world problems in
telecommunications and multimodal transportation networks. Several metaheuristics are
proposed and evaluated. The approaches are compared to the widely adopted Pilot Method
and it is shown that the Variable Neighbourhood Search metaheuristic is the most effective
approach to the problem, obtaining high quality solutions in short computational running
times
On application of least-delay variation problem in ethernet networks using SDN concept
The goal of this paper is to present an application idea of SDN in Smart Grids, particularly, in the area of L2 multicast as defined by IEC 61850-9-2. Authors propose an Integer Linear Formulation (ILP) dealing with a Least-Delay-Variation multicast forwarding problem that has a potential to utilize Ethernet networks in a new way. The proposed ILP formulation is numerically evaluated on random graph topologies and results are compared to a shortest path tree approach that is traditionally a product of Spanning Tree Protocols. Results confirm the correctness of the ILP formulation and illustrate dependency of a solution quality on the selected graph models, especially, in a case of scale-free topologies
QoS multicast tree construction in IP/DWDM optical internet by bio-inspired algorithms
Copyright @ Elsevier Ltd. All rights reserved.In this paper, two bio-inspired Quality of Service (QoS) multicast algorithms are proposed in IP over dense wavelength division multiplexing (DWDM) optical Internet. Given a QoS multicast request and the delay interval required by the application, both algorithms are able to find a flexible QoS-based cost suboptimal routing tree. They first construct the multicast trees based on ant colony optimization and artificial immune algorithm, respectively. Then a dedicated wavelength assignment algorithm is proposed to assign wavelengths to the trees aiming to minimize the delay of the wavelength conversion. In both algorithms, multicast routing and wavelength assignment are integrated into a single process. Therefore, they can find the multicast trees on which the least wavelength conversion delay is achieved. Load balance is also considered in both algorithms. Simulation results show that these two bio-inspired algorithms can construct high performance QoS routing trees for multicast applications in IP/DWDM optical Internet.This work was supported in part ny the Program for New Century Excellent Talents in University, the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1, the National Natural Science Foundation of China under Grant no. 60673159 and 70671020, the National High-Tech Reasearch and Development Plan of China under Grant no. 2007AA041201, and the Specialized Research Fund for the Doctoral Program of Higher Education under Grant no. 20070145017
Finding undetected protein associations in cell signaling by belief propagation
External information propagates in the cell mainly through signaling cascades
and transcriptional activation, allowing it to react to a wide spectrum of
environmental changes. High throughput experiments identify numerous molecular
components of such cascades that may, however, interact through unknown
partners. Some of them may be detected using data coming from the integration
of a protein-protein interaction network and mRNA expression profiles. This
inference problem can be mapped onto the problem of finding appropriate optimal
connected subgraphs of a network defined by these datasets. The optimization
procedure turns out to be computationally intractable in general. Here we
present a new distributed algorithm for this task, inspired from statistical
physics, and apply this scheme to alpha factor and drug perturbations data in
yeast. We identify the role of the COS8 protein, a member of a gene family of
previously unknown function, and validate the results by genetic experiments.
The algorithm we present is specially suited for very large datasets, can run
in parallel, and can be adapted to other problems in systems biology. On
renowned benchmarks it outperforms other algorithms in the field.Comment: 6 pages, 3 figures, 1 table, Supporting Informatio
Discrete Particle Swarm Optimization for the minimum labelling Steiner tree problem
Particle Swarm Optimization is an evolutionary method inspired by the
social behaviour of individuals inside swarms in nature. Solutions of the problem are
modelled as members of the swarm which fly in the solution space. The evolution is
obtained from the continuous movement of the particles that constitute the swarm
submitted to the effect of the inertia and the attraction of the members who lead the
swarm. This work focuses on a recent Discrete Particle Swarm Optimization for combinatorial optimization, called Jumping Particle Swarm Optimization. Its effectiveness is
illustrated on the minimum labelling Steiner tree problem: given an undirected labelled
connected graph, the aim is to find a spanning tree covering a given subset of nodes,
whose edges have the smallest number of distinct labels
Bi-velocity discrete particle swarm optimization and its application to multicast routing problem in communication networks
This paper proposes a novel bi-velocity discrete particle swarm optimization (BVDPSO) approach and extends its application to the NP-complete multicast routing problem (MRP). The main contribution is the extension of PSO from continuous domain to the binary or discrete domain. Firstly, a novel bi-velocity strategy is developed to represent possibilities of each dimension being 1 and 0. This strategy is suitable to describe the binary characteristic of the MRP where 1 stands for a node being selected to construct the multicast tree while 0 stands for being otherwise. Secondly, BVDPSO updates the velocity and position according to the learning mechanism of the original PSO in continuous domain. This maintains the fast convergence speed and global search ability of the original PSO. Experiments are comprehensively conducted on all of the 58 instances with small, medium, and large scales in the OR-library (Operation Research Library). The results confirm that BVDPSO can obtain optimal or near-optimal solutions rapidly as it only needs to generate a few multicast trees. BVDPSO outperforms not only several state-of-the-art and recent heuristic algorithms for the MRP problems, but also algorithms based on GA, ACO, and PSO
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Inference of single-cell phylogenies from lineage tracing data using Cassiopeia.
The pairing of CRISPR/Cas9-based gene editing with massively parallel single-cell readouts now enables large-scale lineage tracing. However, the rapid growth in complexity of data from these assays has outpaced our ability to accurately infer phylogenetic relationships. First, we introduce Cassiopeia-a suite of scalable maximum parsimony approaches for tree reconstruction. Second, we provide a simulation framework for evaluating algorithms and exploring lineage tracer design principles. Finally, we generate the most complex experimental lineage tracing dataset to date, 34,557 human cells continuously traced over 15 generations, and use it for benchmarking phylogenetic inference approaches. We show that Cassiopeia outperforms traditional methods by several metrics and under a wide variety of parameter regimes, and provide insight into the principles for the design of improved Cas9-enabled recorders. Together, these should broadly enable large-scale mammalian lineage tracing efforts. Cassiopeia and its benchmarking resources are publicly available at www.github.com/YosefLab/Cassiopeia
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